Dropout is a standard choice for regularizing a network. We employ it in a channel-wise fashion, that is, we sample a 1D mask of size \(C\)  (with dropout probability \(p_d=0.25\) ) and we broadcast it along the time and frequency axes. Channel-wise dropout is chosen because of its Bayesian interpretation as model averaging between models with variable number of convolutional filters [1]}.
